THE LONGSTAFF-SCHWARTZ ALGORITHM FOR LEVY MODELS: RESULTS ON FAST AND SLOW CONVERGENCE

• 成果类型: Article
• 署名作者: Gerhold, Stefan
• 署名单位: Technische Universitat Wien
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/10-AAP704
• 发表日期: 2011
• 页码: 589-608
• 关键词: Asymptotic Normality; american; bounds; rates

摘要

We investigate the Longstaff-Schwartz algorithm for American option pricing assuming that both the number of regressors and the number of Monte Carlo paths tend to infinity. Our main results concern extensions, respectively, applications of results by Glasserman and Yu [Ann. Appl. Probab. 14 (2004) 2090-2119] and Stentoft [Manag. Sci. 50 (2004) 1193-1203] to several Levy models, in particular the geometric Meixner model. A convenient setting to analyze this convergence problem is provided by the Levy-Sheffer systems introduced by Schoutens and Teugels.